ECML/PKDD'02 Workshop on
"Mining Official Data" (MOD'02)
19th or 20th August 2002
Helsinki, Finland
http://ecmlpkdd.cs.helsinki.fi/mod-2002.html
before the
13th European Conference on Machine Learning (ECML'02)
6th European Conf. on Principles & Practice of Knowledge Discovery in
Databases (PKDD'02)
Helsinki, Finland
under the auspices of the
KDnet - the European Network of Excellence in Knowledge Discovery
TECHNICAL DESCRIPTION
In statistics, the term "official data" denotes data collected in censuses
and statistical surveys by National Statistics Institutes (NSIs), as well as
administrative and registration records collected by government departments
and local authorities. They are used to produce "official statistics" for
the purpose of making policy decisions, and to facilitate the appreciation
of economic, social, demographic, and other matters of interest to the
governments, government departments, local authorities, businesses, and to
the general public. For instance, population and economic census information
is of great value in planning public services (education, fund allocation,
public transport), as well as in private businesses (placing new factories,
shopping malls, or banks, as well as marketing particular products).
Moreover, survey data on specific topics, such as labour force, time use,
household budget, are regularly collected by NSIs to keep updated
information on some economic and social phenomena.
The application of data mining techniques to official data has great
potential in supporting good public policy and in underpinning the effective
functioning of a democratic society. Nevertheless, it is not straightforward
and requires a challenging methodological research, which is still in an
initial stage. In particular, to develop successful applications of data
mining techniques to official data, the following issues must be dealt with:
a. Aggregated data. NSIs make a great effort in collecting census data, but
they are not the only organizations that analyse them: data analysis is
often done by different institutes. By law, NSIs are prohibited from
releasing individual responses to any other government agency or to any
individual or business, so data are aggregated for reasons of privacy before
being distributed to external agencies and institutes. Data analysts are
confronted with the problem of processing data that go beyond the classical
framework, as in the case of data concerning more or less homogeneous
classes or groups of individuals (second-order objects or macro-data),
instead of single individuals (first-order objects or micro-data). The
extension of classical data analysis techniques to the analysis of
second-order objects is one of the main goals of a novel research field
named "symbolic data analysis".
b. Data quality. There are different ways to determine data quality,
including the percentage of errors in data and the use of an unbiased
sampling procedure. One problem with official data is the human error
involved in inputting. Some outlier detection techniques developed in data
mining can be used to find errors in collected data. Data can also be
missing, which is the biggest problem with official data. In this case,
clustering methods can be used to replace missing values. Finally, data
mining techniques can also be used to control the sample bias, before
disseminating official aggregated data used in further processing.
c. Timeliness. This can be considered another aspect of data quality. Public
and private institutions are currently urged to reduce the delay between the
time of data collection and the moment in which decisions are made according
to some statistical indicators. A typical example is the inflation rate
computed by the European Institute of Statistics
(Eurostat) and the decision made by the Central Bank of Europe (BCE) on the
tax rate. A timely delivery of data analysis results may involve the
synthesis of new indicators from official data, the design of different
infrastructures for timely data collection, or the application of 'anytime
algorithms', which provide the data miner with a ready-to-use
model at any time after the first few examples are seen and guarantee a
smooth quality, increasing with time.
d. Geo-referentiation. The practice of geo-referencing census data has
increasingly spread over the last few decades and the techniques for
attaching socio-economic data to specific locations have markedly improved
at the same time. In the UK, for instance, household expenditure data are
provided for each enumeration district (ED), the smallest areal unit for
which census data are published. At the same time, vectorized boundaries of
the 1991 census EDs enable the investigation of socio-economic phenomena in
association with the geographical location of EDs. These advances cause a
growing demand
for more powerful data analysis techniques that can link population data to
their spatial distribution.
e. Metadata. In statistics, metadata concerns the information used for the
most correct understanding of statistical data and their related analysis.
They mainly refer to explanations-definitions-procedures that are followed
from the designing
phase up to the phase of a publication of survey's results. Examples of
metadata are the various statistical populations, sampling techniques,
definitions of nomenclatures, classifications, monetary units and so on. The
basic use of metadata is in interpreting and validating data, as well as in
finding and accessing relevant information. However, metadata can also be
used for analysis purposes. This is notably the case of research studies
performed on government and official statistics, since macro data are of
little use to any data consumer if they are not accompanied by additional
information, such as what they represent, how they were collected and
manipulated and so on.
TOPICS
The workshop will maintain a balance between theoretical issues and
descriptions of case studies to promote synergy between theory and practice.
Research contributions are welcome even though they have not been tested on
"official data" but have a clear relation with some of the research issues
reported in the technical description. Topics of interest include, but are
not limited to:
* Methodologies and policies for the analysis of official data
* Confidentiality protection in data mining
* Mining aggregated data
* Symbolic data analysis
* Data mining for quality control in data capture and transformation
* Data mining techniques for outlier detection
* Data mining techniques for qualitative comparison of statistics
* Infrastructures for timely collection/delivery of official data/statistics
* Anytime data mining algorithms for timely delivery of official statistics
* Spatial data mining of official/business data
* Infrastructures for the provision of metadata
* Use of meta-data in data mining techniques
* Application of meta-data to the validation of data mining results
* Case studies of mining official data
* Descriptions of official data sources and related data mining problems.
WORKSHOP STRUCTURE AND ATTENDANCE
The workshop aims to be a highly communicative meeting place for researchers
working on similar topics, but coming from different communities. In order
to achieve these goals, the workshop will consist of two invited talks,
followed by short
presentations and longer discussions. Each author will be encouraged to read
another accepted paper and to
comment on it after the original talk has been given.
All ECML/PKDD'02 MOD workshop participants must also register for the main
ECML/PKDD conference. Workshop attendance will be limited to registered
participants.
SUBMISSION PROCEDURE
Authors are invited to submit original research contributions or experience
reports in English. Submitted papers must be unpublished and substantially
different from papers under review. Papers that have been or will be
presented at small
workshops/symposia whose proceedings are available only to the attendees may
be submitted. Papers should be double-spaced and no longer than 5000 words
(about 12 single-spaced pages). Papers should be sent electronically
(postscript or pdf) not later than May 24, 2002 to
[EMAIL PROTECTED]
Papers will be selected on the basis of review of full paper contributions.
Authors should make certain that the data mining techniques they describe
deal with the special issues that are associated with official data.
Notification of acceptance will be given by June 14, 2002. Final
camera-ready copies of accepted papers will be due by June 28,
2002. The proceedings will be printed by the ECML/PKDD organizers and
distributed at the workshop. A web-publication of the proceedings is
expected after the conference.
STYLE GUIDE
There is a joint paper style for the proceedings of all ECML/PKDD workshops.
Submitted papers should be formatted according to the Springer-Verlag
Lecture Notes in Artificial Intelligence guidelines. Authors' instructions
and style files can be downloaded from
http://www.springer.de/comp/lncs/authors.html.
IMPORTANT DATES
Submission deadline: May 24, 2002
Notification of acceptance: June 14, 2002
Camera ready copies of papers: June 28, 2002
Workshop: August 19/20, 2002
ORGANIZING COMMITTEE
This workshop will be organized by:
Paula Brito, Faculty of Economics, University of Porto, Portugal
Donato Malerba, Department of Informatics, University of Bari, Italy
PROGRAM COMMITTEE
Timo Alanko, Statistical R&D Unit, Statistics Finland, Helsinki, Finland
Edwin Diday, CEREMADE, Paris-9 Dauphine University, Paris, France
Floriana Esposito, Department of Informatics, University of Bari, Italy
Paulo Gomes, National Institute of Statistics (INE), Lisbon, Portugal
Haralambos Papageorgiou, Department of Mathematics, University of Athens,
Athens, Greece
Willi Kl�sgen, Fraunhofer Institute for Autonomous Intelligent Systems,
Sankt Augustin, Germany
Carlos Marcelo, National Institute of Statistics (INE), Lisbon, Portugal
Michael May, Fraunhofer Institute for Autonomous Intelligent Systems, Sankt
Augustin, Germany
Monique Noirhomme, Institut d'Informatique, University Notre-Dame de la
Paix, Namur, Belgium
Mireille Summa, CEREMADE, Paris-9 Dauphine University, Paris, France
Ian Turton, Centre for Computational Geography, University of Leeds, Leeds,
UK,
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